融合路侧摄像头与激光雷达数据，提升施工区车辆追踪精度与鲁棒性。
Infrastructure Sensor-enabled Vehicle Data Generation using Multi-Sensor Fusion for Proactive Safety Applications at Work Zone
- 采用卡尔曼滤波后融合策略，整合多传感器数据
- 纵向误差降低70%，横向定位保持1-3米内
- 适用于复杂交通环境的主动安全系统部署
基于基础设施的感知与实时轨迹生成在高风险道路段（如施工区）中展现出提升安全性的潜力，但实际应用受限于视角失真、复杂几何结构、遮挡及成本问题。本研究通过将路边摄像头与激光雷达集成至共仿真环境，构建可扩展、低成本的车辆检测与定位框架，并采用基于卡尔曼滤波的后融合策略，提升轨迹一致性与准确性。仿真结果显示，融合算法相较单个传感器将纵向误差降低最高达70%，同时保持横向精度在1至3米之间。在真实施工区进行的现场验证中，以激光雷达、雷达-相机组合及RTK-GPS作为真值，结果表明融合轨迹即使在单一传感器数据间歇或退化时仍能紧密匹配实际车迹。这些结果证实，基于卡尔曼滤波的传感器融合可有效弥补单个传感器缺陷，提供精确且稳健的车辆追踪能力。该方法为在复杂交通环境中部署基础设施驱动的多传感器系统以实现主动安全措施提供了可行路径。
原文摘要 · Abstract (English)
Infrastructure-based sensing and real-time trajectory generation show promise for improving safety in high-risk roadway segments such as work zones, yet practical deployments are hindered by perspective distortion, complex geometry, occlusions, and costs. This study tackles these barriers by integrating roadside camera and LiDAR sensors into a cosimulation environment to develop a scalable, cost-effective vehicle detection and localization framework, and employing a Kalman Filter-based late fusion strategy to enhance trajectory consistency and accuracy. In simulation, the fusion algorithm reduced longitudinal error by up to 70 percent compared to individual sensors while preserving lateral accuracy within 1 to 3 meters. Field validation in an active work zone, using LiDAR, a radar-camera rig, and RTK-GPS as ground truth, demonstrated that the fused trajectories closely match real vehicle paths, even when single-sensor data are intermittent or degraded. These results confirm that KF based sensor fusion can reliably compensate for individual sensor limitations, providing precise and robust vehicle tracking capabilities. Our approach thus offers a practical pathway to deploy infrastructure-enabled multi-sensor systems for proactive safety measures in complex traffic environments.
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